
Given a complete and accurate URDF (all kinematic and dynamic parameters are accurately specified): The library can be utilized as ground truth model within model-based RL algorithms or trajectory optimization settings [1]}, [2]} and enables batching and leveraging of GPU resources.

Given an incomplete URDF or the goal is to study learning algorithms: We can learn any unknown parameters of the robot from observations by harnessing the power of automatic differentiation. For instance, it is possible to identify the dynamics parameters of the analytical dynamics model and compare to learning other dynamics representations with varying degrees of structure [3]}.

Because this library implements the analytical (ground truth) models of robots within a modern deep learning framework, it can support the pursuit of finding the "convolutional neural network" of robot motion.

Finally, this library can be utilized as a teaching tool that allows to teach core robotics concepts such as kinematics in the context of modern deep learning frameworks.

